Abstract

Data rate and amount of SAR data exceed the capacity of usual data links and storage devices in most cases. SAR data compression is necessary to ensure efficient use of acquired data and processed images. Unfortunately, lossless compression is not possible for SAR data due to their high entropy. However, it is of major importance that no SAR applications suffer from data compression. There are several strategies: 1) nearly lossless data compression with a very high overall and local performance: Quantization errors do not exceed a certain (small) value for any objects in the image, the drawback is, of course, the only small compression ratio; 2) data compression adaptive to the image contents a) The coder uses more bits to quantize coefficients of regions with high activity compared to those of more homogeneous regions. For example, in SAR images point targets and edges are treated separately from the other image parts. This procedure allows a medium overall compression ratio and gives a homogeneous error distribution over the whole image. b) The coder uses more bits for regions of interest. Whereas methods 1 and 2a allow any application on the decompressed SAR data without loss in performance, method 2b gives the opportunity to adapt compression to a specific application and a very high compression ratio can be achieved. In this paper we propose the combination of these methods to take optimal advantage of the whole SAR system consisting of the SAR sensor itself, the network of ground stations with high computational archiving resources and clients with usually limited computational power and low capacity links.